[R-meta] account for uncertainty of predictors in meta-analysis

Dear Reza,

Thank you for demonstrating this. If I may ask a follow up question,
are values like vvc necessary for the accurate estimation of the
regression coefficient in matreg's output?

Thanks again,
Simon

On Tue, May 30, 2023 at 11:35?PM Reza Norouzian via
R-sig-meta-analysis <r-sig-meta-analysis at r-project.org> wrote:

Yefeng,

Along the same lines, I believe metafor gained the matreg() function a
while back for conducting *post-hoc* latent regression from rma.mv()
models. Using this approach, you can regress any of your outcome
categories on another one and obtain a regression coefficient for it
(code below).

Kind regards,
Reza

V <- vcalc(vi=1, cluster=author, rvars=c(v1i, v2i), data=dat.berkey1998)

mvml = rma.mv(yi, V, mods = ~ outcome + 0,
              random = ~ outcome | trial, struct="UN",
              data=dat.berkey1998,
              method="ML", cvvc="varcov", control=list(nearpd=TRUE))

# Predicting AL from PD:
matreg(y="AL", x="PD", R=mvml$G, cov=TRUE, means=coef(mvml), V=mvml$vvc)

# Predicting PD from AL:
matreg(y="PD", x="AL", R=mvml$G, cov=TRUE, means=coef(mvml), V=mvml$vvc)


On Mon, May 29, 2023 at 3:43?AM Mike Cheung via R-sig-meta-analysis
<r-sig-meta-analysis at r-project.org> wrote:

Hi Yefeng,

Covariates in meta-regression are treated as a design matrix. I do not

see

how it can handle covariates with sampling variances.

A structural equation modeling (SEM) approach can easily handle it.

You may

refer to

https://stats.stackexchange.com/questions/58310/can-i-include-an-effect-size-as-an-independent-variable-in-a-meta-regression/58534

for a discussion.

Best,
Mike

On Sun, May 28, 2023 at 7:42?PM Yefeng Yang via R-sig-meta-analysis <
r-sig-meta-analysis at r-project.org> wrote:

Dear community,

Do any experts have any ideas on how to use univariate methods to

quantify

the (bivariate) relationship between the two true outcomes? I know
multivariate meta-analysis can do this. But I am asking whether it is
possible to use any univariate methods to do this. See the details

below

based on an example dataset from metafor.

Suppose my dataset has two outcomes PD and AL, which are contained

in the

column "outcome" in the dataset. Now I want to estimate the

correlation or

covariance between PD and AL.

The multivariate approach is as follows:
dat <- dat.berkey1998 # dataset from metafor
rma.mv(yi, V, mods = ~ outcome - 1, random = ~ outcome | trial,
struct="UN", data=dat)
The correlation between the random effects in the output is the

parameter

of my interest.

If we reshape the dataset to create two columns to contain PD and AL,
separately, we can use an univariate method to estimate the

correlation

between them:
rma.mv(PD ~ AL, V, random = ~ 1 | study/trial, data=dat)

But in this way, we do not account for the uncertainty in AL. Or more
precisely, the sampling variance in AL is not accounted for. So the
estimated model coefficient is a sort of overall correlation between

PD and

AL, which is a sort of weighted average of correlation between true

PD and

AL and estimated PD and AL. Except for the Bayesian method (which

uses the

trick of measurement error), any solutions for this? This question

can be

generalized as when using estimated effect size or outcomes as

predictors

in the context of meta-analysis, what are the potential or best

practices?

Very much appreciate any comments.

Best,
Yefeng


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